<p>Climate change in the polar regions exerts a profound influence both locally and over all of our planet.  Physical and ecosystem changes influence societies and economies, via factors that include food provision, transport and access to non-renewable resources.  Sea level, global climate and potentially mid-latitude weather are influenced by the changing polar regions, through coupled feedback processes, sea ice changes and the melting of snow and land-based ice sheets and glaciers.</p><p>Reflecting this importance, the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) features a chapter highlighting past, ongoing and future change in the polar regions, the impacts of these changes, and the possible options for response.  The role of the polar oceans, both in determining the changes and impacts in the polar regions and in structuring the global influence, is an important component of this chapter.</p><p>With emphasis on the Southern Ocean and through comparison with the Arctic, this talk will outline key findings from the polar regions chapter of SROCC. It will synthesise the latest information on the rates, patterns and causes of changes in sea ice, ocean circulation and properties. It will assess cryospheric driving of ocean change from ice sheets, ice shelves and glaciers, and the role of the oceans in determining the past and future evolutions of polar land-based ice. The implications of these changes for climate, ecosystems, sea level and the global system will be outlined.</p>

Abstract. This paper presents an analysis of observed and simulated historical snow cover extent and snow mass, along with future snow cover projections from models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 6 (CMIP6). Where appropriate, the CMIP6 output is compared to CMIP5 results in order to assess progress (or absence thereof) between successive model generations. An ensemble of six observation-based products is used to produce a new time series of historical Northern Hemisphere snow extent anomalies and trends; a subset of four of these products is used for snow mass. Trends in snow extent over 1981–2018 are negative in all months and exceed -50×103 km2 yr−1 during November, December, March, and May. Snow mass trends are approximately −5 Gt yr−1 or more for all months from December to May. Overall, the CMIP6 multi-model ensemble better represents the snow extent climatology over the 1981–2014 period for all months, correcting a low bias in CMIP5. Simulated snow extent and snow mass trends over the 1981–2014 period are stronger in CMIP6 than in CMIP5, although large inter-model spread remains in the simulated trends for both variables. There is a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all CMIP6 Shared Socioeconomic Pathways. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8 % relative to the 1995–2014 level per degree Celsius of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast-response components of the cryosphere such as sea ice and near-surface permafrost extent.

Variation in snow albedo feedback (SAF) among Coupled Model Intercomparison Project phase 5 climate models has been shown to explain much of the variation in projected 21st century warming over Northern Hemisphere land. Prior studies using observations and models have demonstrated both considerable spread in the albedo and a negative bias in the simulated strength of SAF, over snow-covered boreal forests. Boreal evergreen needleleaf forests are capable of intercepting snowfall throughout the winter and consequently exert a significant impact on seasonal surface albedo. Two satellite data products and tower-based observations of albedo are compared with simulations from multiple versions of the Community Climate System Model (CCSM4) to investigate the causes of weak simulated SAF over the boreal forest. The largest bias occurs in April and May, when simulated SAF is one half the strength of SAF in observations. This is traced to two features of the canopy snow parameterizations used in the land model. First, there is no mechanism for the dynamic removal of snow from the canopy when temperatures are below freezing, which results in albedo values in midwinter that are biased high. Second, when temperatures do rise above freezing, all snow on the canopy is melted instantaneously, which results in an unrealistically early transition from a snow-covered to a snow-free canopy. These processes combine to produce large differences between simulated and observed monthly albedo and are the source of the weak bias in SAF. This analysis highlights the importance of canopy snow parameterizations for simulating the hemispheric scale climate response to surface albedo perturbations.

Abstract. Local-scale variations in snow density and layering on Arctic sea ice were characterized using a combination of traditional snow pit and SnowMicroPen (SMP) measurements. In total, 14 sites were evaluated within the Canadian Arctic Archipelago and Arctic Ocean on both first-year (FYI) and multi-year (MYI) sea ice. Sites contained multiple snow pits with coincident SMP profiles as well as unidirectional SMP transects. An existing SMP density model was recalibrated using manual density cutter measurements (n=186) to identify best-fit parameters for the observed conditions. Cross-validation of the revised SMP model showed errors comparable to the expected baseline for manual density measurements (RMSE = 34 kg m−3 or 10.9 %) and strong retrieval skill (R2=0.78). The density model was then applied to SMP transect measurements to characterize variations at spatial scales of up to 100 m. A supervised classification trained on snow pit stratigraphy allowed separation of the SMP density estimates by layer type. The resulting dataset contains 58 882 layer-classified estimates of snow density on sea ice representing 147 m of vertical variation and equivalent to more than 600 individual snow pits. An average bulk density of 310 kg m−3 was estimated with clear separation between FYI and MYI environments. Lower densities on MYI (277 kg m−3) corresponded with increased depth hoar composition (49.2 %), in strong contrast to composition of the thin FYI snowpack (19.8 %). Spatial auto-correlation analysis showed layered composition on FYI snowpack to persist over long distances while composition on MYI rapidly decorrelated at distances less than 16 m. Application of the SMP profiles to determine propagation bias in radar altimetry showed the potential errors of 0.5 cm when climatology is used over known snow density.

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